{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/ArthurFDLR/pose-classification-kit/blob/master/examples/body_pose_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Gt3BWL5uRUfB"
   },
   "source": [
    "# 🕺 Pose Classification Kit: Body pose classification model creation\n",
    "\n",
    "This Notebook can be used to create Neural Network classifiers running in the [Pose Classification Kit](https://github.com/ArthurFDLR/pose-classification-kit).\n",
    "\n",
    "First, we have to import several libraries to create and train a new model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qzConJTSOWl_",
    "outputId": "4d368e8a-1be3-4f11-d6f1-754c5ed00cc4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pose-classification-kit in /usr/local/lib/python3.7/dist-packages (1.1.5)\n",
      "Requirement already satisfied: numpy<1.20.0,>=1.19.2 in /usr/local/lib/python3.7/dist-packages (from pose-classification-kit) (1.19.5)\n",
      "Requirement already satisfied: pandas<2.0.0,>=1.1.5 in /usr/local/lib/python3.7/dist-packages (from pose-classification-kit) (1.1.5)\n",
      "Requirement already satisfied: tensorflow in /usr/local/lib/python3.7/dist-packages (from pose-classification-kit) (2.5.0)\n",
      "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas<2.0.0,>=1.1.5->pose-classification-kit) (2018.9)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas<2.0.0,>=1.1.5->pose-classification-kit) (2.8.1)\n",
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      "Requirement already satisfied: wrapt~=1.12.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow->pose-classification-kit) (1.12.1)\n",
      "Requirement already satisfied: keras-nightly~=2.5.0.dev in /usr/local/lib/python3.7/dist-packages (from tensorflow->pose-classification-kit) (2.5.0.dev2021032900)\n",
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      "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard~=2.5->tensorflow->pose-classification-kit) (3.0.4)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard~=2.5->tensorflow->pose-classification-kit) (2.10)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.5->tensorflow->pose-classification-kit) (3.1.1)\n",
      "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->markdown>=2.6.8->tensorboard~=2.5->tensorflow->pose-classification-kit) (3.5.0)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "from matplotlib.lines import Line2D\n",
    "plt.style.use('ggplot')\n",
    "import os\n",
    "\n",
    "try:\n",
    "    import google.colab\n",
    "    IN_COLAB = True\n",
    "except:\n",
    "    IN_COLAB = False\n",
    "\n",
    "if IN_COLAB:\n",
    "    %tensorflow_version 2.x\n",
    "    !pip install pose-classification-kit\n",
    "    \n",
    "import tensorflow\n",
    "from tensorflow import keras\n",
    "from pose_classification_kit.datasets import BODY18, bodyDataset, dataAugmentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uHIyJdyqRkcl"
   },
   "source": [
    "## Import dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0SK3FpRtPnwV",
    "outputId": "b5437809-fbb7-4541-fea3-f3b3d314adcf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((8040, 18, 2), (8040, 20))"
      ]
     },
     "execution_count": 2,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = bodyDataset(testSplit=.2, shuffle=True, bodyModel=BODY18)\n",
    "x_train = dataset['x_train']\n",
    "y_train = dataset['y_train_onehot']\n",
    "\n",
    "x_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ujWOy4Z8Ru4Q"
   },
   "source": [
    "## Data augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HlNjhLolQWmd",
    "outputId": "7ff55e7f-9fff-47cc-ff65-6b1d134a71fb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((13668, 18, 2), (13668, 20))"
      ]
     },
     "execution_count": 3,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x, y = [x_train], [y_train]\r\n",
    "\r\n",
    "# Scaling augmentation\r\n",
    "x[len(x):],y[len(y):] = tuple(zip(dataAugmentation(\r\n",
    "    x_train, y_train,\r\n",
    "    augmentation_ratio=.1,\r\n",
    "    scaling_factor_standard_deviation=.08,\r\n",
    "    #random_noise_standard_deviation=.03,\r\n",
    ")))\r\n",
    "\r\n",
    "# Rotation augmentation\r\n",
    "x[len(x):],y[len(y):] = tuple(zip(dataAugmentation(\r\n",
    "    x_train, y_train,\r\n",
    "    augmentation_ratio=.1,\r\n",
    "    rotation_angle_standard_deviation=10,\r\n",
    "    #random_noise_standard_deviation=.03\r\n",
    ")))\r\n",
    "\r\n",
    "# Upper-body augmentation\r\n",
    "lowerBody_keypoints = np.where(np.isin(BODY18.mapping,[\r\n",
    "    \"left_knee\", \"right_knee\", \"left_ankle\", \"right_ankle\" #,\"left_hip\", \"right_hip\",\r\n",
    "]))[0]\r\n",
    "x[len(x):],y[len(y):] = tuple(zip(dataAugmentation(\r\n",
    "    x_train, y_train,\r\n",
    "    augmentation_ratio=.15,\r\n",
    "    remove_specific_keypoints=lowerBody_keypoints,\r\n",
    "    random_noise_standard_deviation=.03\r\n",
    ")))\r\n",
    "lowerBody_keypoints = np.where(np.isin(BODY18.mapping,[\r\n",
    "    \"left_knee\", \"right_knee\", \"left_ankle\", \"right_ankle\", \"left_hip\", \"right_hip\",\r\n",
    "]))[0]\r\n",
    "x[len(x):],y[len(y):] = tuple(zip(dataAugmentation(\r\n",
    "    x_train, y_train,\r\n",
    "    augmentation_ratio=.15,\r\n",
    "    remove_specific_keypoints=lowerBody_keypoints,\r\n",
    "    random_noise_standard_deviation=.03\r\n",
    ")))        \r\n",
    "\r\n",
    "# Random partial input augmentation\r\n",
    "x[len(x):],y[len(y):] = tuple(zip(dataAugmentation(\r\n",
    "    x_train, y_train,\r\n",
    "    augmentation_ratio=.2,\r\n",
    "    remove_rand_keypoints_nbr=2,\r\n",
    "    random_noise_standard_deviation=.03\r\n",
    ")))\r\n",
    "\r\n",
    "x_train_augmented = np.concatenate(x, axis=0)\r\n",
    "y_train_augmented = np.concatenate(y, axis=0)\r\n",
    "\r\n",
    "x_train_augmented.shape, y_train_augmented.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vlWaRyQQSOfO"
   },
   "source": [
    "## Models exploration\n",
    "\n",
    "This section is optional. The following blocks can be used to compare different architecture and training processes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "5WWSNnJ1SRgK"
   },
   "outputs": [],
   "source": [
    "model_train_history = {}\r\n",
    "input_dim = x_train.shape[1:]\r\n",
    "output_dim = len(dataset['labels'])\r\n",
    "validation_split = 0.20\r\n",
    "epochs = 15"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jjGMPJ5psqL0"
   },
   "source": [
    "### Architectures comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7uEtaKr5fmYM",
    "outputId": "e66375a6-d3b2-4554-c9ff-4fe957c1b522"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"Light_ANN\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "flatten (Flatten)            (None, 36)                0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 48)                1776      \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 48)                2352      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 20)                980       \n",
      "=================================================================\n",
      "Total params: 5,108\n",
      "Trainable params: 5,108\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/15\n",
      "402/402 [==============================] - 1s 2ms/step - loss: 1.9572 - accuracy: 0.5227 - val_loss: 0.9086 - val_accuracy: 0.8234\n",
      "Epoch 2/15\n",
      "402/402 [==============================] - 0s 1ms/step - loss: 0.5643 - accuracy: 0.8831 - val_loss: 0.3706 - val_accuracy: 0.9272\n",
      "Epoch 3/15\n",
      "402/402 [==============================] - 1s 1ms/step - loss: 0.2972 - accuracy: 0.9286 - val_loss: 0.2417 - val_accuracy: 0.9484\n",
      "Epoch 4/15\n",
      "402/402 [==============================] - 0s 1ms/step - loss: 0.2056 - accuracy: 0.9490 - val_loss: 0.1754 - val_accuracy: 0.9683\n",
      "Epoch 5/15\n",
      "402/402 [==============================] - 0s 1ms/step - loss: 0.1593 - accuracy: 0.9639 - val_loss: 0.1326 - val_accuracy: 0.9733\n",
      "Epoch 6/15\n",
      "402/402 [==============================] - 0s 1ms/step - loss: 0.1252 - accuracy: 0.9739 - val_loss: 0.1131 - val_accuracy: 0.9782\n",
      "Epoch 7/15\n",
      "402/402 [==============================] - 1s 1ms/step - loss: 0.1052 - accuracy: 0.9768 - val_loss: 0.0902 - val_accuracy: 0.9820\n",
      "Epoch 8/15\n",
      "402/402 [==============================] - 1s 1ms/step - loss: 0.0873 - accuracy: 0.9821 - val_loss: 0.0854 - val_accuracy: 0.9807\n",
      "Epoch 9/15\n",
      "402/402 [==============================] - 0s 1ms/step - loss: 0.0771 - accuracy: 0.9831 - val_loss: 0.0686 - val_accuracy: 0.9876\n",
      "Epoch 10/15\n",
      "402/402 [==============================] - 0s 1ms/step - loss: 0.0673 - accuracy: 0.9851 - val_loss: 0.0599 - val_accuracy: 0.9882\n",
      "Epoch 11/15\n",
      "402/402 [==============================] - 0s 1ms/step - loss: 0.0592 - accuracy: 0.9866 - val_loss: 0.0554 - val_accuracy: 0.9894\n",
      "Epoch 12/15\n",
      "402/402 [==============================] - 0s 1ms/step - loss: 0.0534 - accuracy: 0.9882 - val_loss: 0.0518 - val_accuracy: 0.9882\n",
      "Epoch 13/15\n",
      "402/402 [==============================] - 0s 1ms/step - loss: 0.0492 - accuracy: 0.9894 - val_loss: 0.0472 - val_accuracy: 0.9907\n",
      "Epoch 14/15\n",
      "402/402 [==============================] - 0s 1ms/step - loss: 0.0459 - accuracy: 0.9882 - val_loss: 0.0463 - val_accuracy: 0.9913\n",
      "Epoch 15/15\n",
      "402/402 [==============================] - 0s 1ms/step - loss: 0.0431 - accuracy: 0.9914 - val_loss: 0.0430 - val_accuracy: 0.9869\n"
     ]
    }
   ],
   "source": [
    "model = keras.models.Sequential(\r\n",
    "    name = 'Light_ANN',\r\n",
    "    layers =\r\n",
    "    [\r\n",
    "        keras.layers.InputLayer(input_shape=input_dim),\r\n",
    "        keras.layers.Flatten(),\r\n",
    "        keras.layers.Dense(48, activation=keras.activations.relu),\r\n",
    "        keras.layers.Dense(48, activation=keras.activations.relu),\r\n",
    "        keras.layers.Dense(output_dim, activation=keras.activations.softmax),\r\n",
    "    ]\r\n",
    ")\r\n",
    "\r\n",
    "model.summary()\r\n",
    "model.compile(\r\n",
    "    optimizer=keras.optimizers.Adam(),\r\n",
    "    loss='categorical_crossentropy',\r\n",
    "    metrics=['accuracy'],\r\n",
    ")\r\n",
    "\r\n",
    "model_train_history[model] = model.fit(\r\n",
    "    x=x_train,\r\n",
    "    y=y_train,\r\n",
    "    epochs=epochs,\r\n",
    "    batch_size=16,\r\n",
    "    validation_split=validation_split,\r\n",
    "    shuffle=True,\r\n",
    "    verbose=1,\r\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "efFpEmAeufGA",
    "outputId": "61880468-4ff1-4e70-f94c-af931de1861c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"Light_CNN\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1d (Conv1D)              (None, 16, 12)            84        \n",
      "_________________________________________________________________\n",
      "conv1d_1 (Conv1D)            (None, 14, 12)            444       \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 168)               0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 20)                3380      \n",
      "=================================================================\n",
      "Total params: 3,908\n",
      "Trainable params: 3,908\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/15\n",
      "402/402 [==============================] - 1s 2ms/step - loss: 1.8955 - accuracy: 0.4845 - val_loss: 0.8772 - val_accuracy: 0.7519\n",
      "Epoch 2/15\n",
      "402/402 [==============================] - 1s 2ms/step - loss: 0.5850 - accuracy: 0.8424 - val_loss: 0.4157 - val_accuracy: 0.8818\n",
      "Epoch 3/15\n",
      "402/402 [==============================] - 1s 2ms/step - loss: 0.3278 - accuracy: 0.9145 - val_loss: 0.2582 - val_accuracy: 0.9117\n",
      "Epoch 4/15\n",
      "402/402 [==============================] - 1s 2ms/step - loss: 0.2333 - accuracy: 0.9411 - val_loss: 0.1957 - val_accuracy: 0.9590\n",
      "Epoch 5/15\n",
      "402/402 [==============================] - 1s 2ms/step - loss: 0.1842 - accuracy: 0.9532 - val_loss: 0.1588 - val_accuracy: 0.9608\n",
      "Epoch 6/15\n",
      "402/402 [==============================] - 1s 2ms/step - loss: 0.1540 - accuracy: 0.9664 - val_loss: 0.1273 - val_accuracy: 0.9720\n",
      "Epoch 7/15\n",
      "402/402 [==============================] - 1s 2ms/step - loss: 0.1272 - accuracy: 0.9726 - val_loss: 0.1046 - val_accuracy: 0.9832\n",
      "Epoch 8/15\n",
      "402/402 [==============================] - 1s 2ms/step - loss: 0.1065 - accuracy: 0.9792 - val_loss: 0.0938 - val_accuracy: 0.9832\n",
      "Epoch 9/15\n",
      "402/402 [==============================] - 1s 2ms/step - loss: 0.0903 - accuracy: 0.9846 - val_loss: 0.0759 - val_accuracy: 0.9832\n",
      "Epoch 10/15\n",
      "402/402 [==============================] - 1s 1ms/step - loss: 0.0843 - accuracy: 0.9835 - val_loss: 0.0705 - val_accuracy: 0.9845\n",
      "Epoch 11/15\n",
      "402/402 [==============================] - 1s 1ms/step - loss: 0.0723 - accuracy: 0.9873 - val_loss: 0.0714 - val_accuracy: 0.9863\n",
      "Epoch 12/15\n",
      "402/402 [==============================] - 1s 2ms/step - loss: 0.0683 - accuracy: 0.9874 - val_loss: 0.0661 - val_accuracy: 0.9857\n",
      "Epoch 13/15\n",
      "402/402 [==============================] - 1s 2ms/step - loss: 0.0612 - accuracy: 0.9896 - val_loss: 0.0566 - val_accuracy: 0.9882\n",
      "Epoch 14/15\n",
      "402/402 [==============================] - 1s 2ms/step - loss: 0.0579 - accuracy: 0.9887 - val_loss: 0.0509 - val_accuracy: 0.9907\n",
      "Epoch 15/15\n",
      "402/402 [==============================] - 1s 1ms/step - loss: 0.0526 - accuracy: 0.9910 - val_loss: 0.0653 - val_accuracy: 0.9813\n"
     ]
    }
   ],
   "source": [
    "model = keras.models.Sequential(\r\n",
    "    name = 'Light_CNN',\r\n",
    "    layers =\r\n",
    "    [\r\n",
    "        keras.layers.InputLayer(input_shape=input_dim),\r\n",
    "        keras.layers.Conv1D(12, 3, activation='relu'),\r\n",
    "        keras.layers.Conv1D(12, 3, activation='relu'),\r\n",
    "        keras.layers.Flatten(),\r\n",
    "        keras.layers.Dense(output_dim, activation=keras.activations.softmax),\r\n",
    "    ]\r\n",
    ")\r\n",
    "\r\n",
    "model.summary()\r\n",
    "model.compile(\r\n",
    "    optimizer=keras.optimizers.Adam(),\r\n",
    "    loss='categorical_crossentropy',\r\n",
    "    metrics=['accuracy'],\r\n",
    ")\r\n",
    "\r\n",
    "model_train_history[model] = model.fit(\r\n",
    "    x=x_train,\r\n",
    "    y=y_train,\r\n",
    "    epochs=epochs,\r\n",
    "    batch_size=16,\r\n",
    "    validation_split=validation_split,\r\n",
    "    shuffle=True,\r\n",
    "    verbose=1,\r\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_BB7m6RgDVFD"
   },
   "source": [
    "### Data augmented training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YAmxtOGJDdZ_",
    "outputId": "d0e2977f-487c-4c00-8761-950452761d78"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"Light_ANN_Augmented\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "flatten_2 (Flatten)          (None, 36)                0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 48)                1776      \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 48)                2352      \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 20)                980       \n",
      "=================================================================\n",
      "Total params: 5,108\n",
      "Trainable params: 5,108\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/15\n",
      "684/684 [==============================] - 1s 1ms/step - loss: 1.5349 - accuracy: 0.5920 - val_loss: 0.9071 - val_accuracy: 0.7169\n",
      "Epoch 2/15\n",
      "684/684 [==============================] - 1s 1ms/step - loss: 0.4119 - accuracy: 0.8935 - val_loss: 0.6899 - val_accuracy: 0.8003\n",
      "Epoch 3/15\n",
      "684/684 [==============================] - 1s 1ms/step - loss: 0.2479 - accuracy: 0.9383 - val_loss: 0.6801 - val_accuracy: 0.8036\n",
      "Epoch 4/15\n",
      "684/684 [==============================] - 1s 1ms/step - loss: 0.1827 - accuracy: 0.9576 - val_loss: 0.7066 - val_accuracy: 0.8168\n",
      "Epoch 5/15\n",
      "684/684 [==============================] - 1s 1ms/step - loss: 0.1470 - accuracy: 0.9645 - val_loss: 0.6911 - val_accuracy: 0.8365\n",
      "Epoch 6/15\n",
      "684/684 [==============================] - 1s 1ms/step - loss: 0.1227 - accuracy: 0.9706 - val_loss: 0.7074 - val_accuracy: 0.8442\n",
      "Epoch 7/15\n",
      "684/684 [==============================] - 1s 1ms/step - loss: 0.1061 - accuracy: 0.9758 - val_loss: 0.7147 - val_accuracy: 0.8394\n",
      "Epoch 8/15\n",
      "684/684 [==============================] - 1s 1ms/step - loss: 0.0961 - accuracy: 0.9775 - val_loss: 0.7199 - val_accuracy: 0.8413\n",
      "Epoch 9/15\n",
      "684/684 [==============================] - 1s 1ms/step - loss: 0.0863 - accuracy: 0.9791 - val_loss: 0.7200 - val_accuracy: 0.8453\n",
      "Epoch 10/15\n",
      "684/684 [==============================] - 1s 1ms/step - loss: 0.0777 - accuracy: 0.9802 - val_loss: 0.7607 - val_accuracy: 0.8369\n",
      "Epoch 11/15\n",
      "684/684 [==============================] - 1s 1ms/step - loss: 0.0738 - accuracy: 0.9816 - val_loss: 0.7743 - val_accuracy: 0.8347\n",
      "Epoch 12/15\n",
      "684/684 [==============================] - 1s 1ms/step - loss: 0.0670 - accuracy: 0.9828 - val_loss: 0.8349 - val_accuracy: 0.8266\n",
      "Epoch 13/15\n",
      "684/684 [==============================] - 1s 1ms/step - loss: 0.0634 - accuracy: 0.9834 - val_loss: 0.7949 - val_accuracy: 0.8189\n",
      "Epoch 14/15\n",
      "684/684 [==============================] - 1s 1ms/step - loss: 0.0593 - accuracy: 0.9854 - val_loss: 0.7936 - val_accuracy: 0.8197\n",
      "Epoch 15/15\n",
      "684/684 [==============================] - 1s 1ms/step - loss: 0.0540 - accuracy: 0.9864 - val_loss: 0.8059 - val_accuracy: 0.8376\n"
     ]
    }
   ],
   "source": [
    "model = keras.models.Sequential(\r\n",
    "    name = 'Light_ANN_Augmented',\r\n",
    "    layers =\r\n",
    "    [\r\n",
    "        keras.layers.InputLayer(input_shape=input_dim),\r\n",
    "        keras.layers.Flatten(),\r\n",
    "        keras.layers.Dense(48, activation=keras.activations.relu),\r\n",
    "        keras.layers.Dense(48, activation=keras.activations.relu),\r\n",
    "        keras.layers.Dense(output_dim, activation=keras.activations.softmax),\r\n",
    "    ]\r\n",
    ")\r\n",
    "\r\n",
    "model.summary()\r\n",
    "model.compile(\r\n",
    "    optimizer=keras.optimizers.Adam(),\r\n",
    "    loss='categorical_crossentropy',\r\n",
    "    metrics=['accuracy'],\r\n",
    ")\r\n",
    "\r\n",
    "model_train_history[model] = model.fit(\r\n",
    "    x=x_train_augmented,\r\n",
    "    y=y_train_augmented,\r\n",
    "    epochs=epochs,\r\n",
    "    batch_size=16,\r\n",
    "    validation_split=validation_split,\r\n",
    "    shuffle=True,\r\n",
    "    verbose=1,\r\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tvclG-0cSgZv",
    "outputId": "b68f0f47-4bdd-4f8a-bb0a-1bfb4db2f3a2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"Light_CNN_Augmented\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1d_2 (Conv1D)            (None, 16, 16)            112       \n",
      "_________________________________________________________________\n",
      "conv1d_3 (Conv1D)            (None, 14, 16)            784       \n",
      "_________________________________________________________________\n",
      "flatten_3 (Flatten)          (None, 224)               0         \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 20)                4500      \n",
      "=================================================================\n",
      "Total params: 5,396\n",
      "Trainable params: 5,396\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/15\n",
      "684/684 [==============================] - 1s 2ms/step - loss: 1.2583 - accuracy: 0.6709 - val_loss: 0.8504 - val_accuracy: 0.7341\n",
      "Epoch 2/15\n",
      "684/684 [==============================] - 1s 1ms/step - loss: 0.2967 - accuracy: 0.9260 - val_loss: 0.8033 - val_accuracy: 0.7868\n",
      "Epoch 3/15\n",
      "684/684 [==============================] - 1s 1ms/step - loss: 0.1851 - accuracy: 0.9526 - val_loss: 0.7896 - val_accuracy: 0.8116\n",
      "Epoch 4/15\n",
      "684/684 [==============================] - 1s 1ms/step - loss: 0.1409 - accuracy: 0.9641 - val_loss: 0.8599 - val_accuracy: 0.8032\n",
      "Epoch 5/15\n",
      "684/684 [==============================] - 1s 1ms/step - loss: 0.1160 - accuracy: 0.9720 - val_loss: 0.8927 - val_accuracy: 0.7871\n",
      "Epoch 6/15\n",
      "684/684 [==============================] - 1s 1ms/step - loss: 0.1022 - accuracy: 0.9732 - val_loss: 0.9288 - val_accuracy: 0.7871\n",
      "Epoch 7/15\n",
      "684/684 [==============================] - 1s 1ms/step - loss: 0.0920 - accuracy: 0.9763 - val_loss: 0.9514 - val_accuracy: 0.8029\n",
      "Epoch 8/15\n",
      "684/684 [==============================] - 1s 1ms/step - loss: 0.0844 - accuracy: 0.9790 - val_loss: 1.0236 - val_accuracy: 0.7948\n",
      "Epoch 9/15\n",
      "684/684 [==============================] - 1s 1ms/step - loss: 0.0750 - accuracy: 0.9808 - val_loss: 0.9760 - val_accuracy: 0.8091\n",
      "Epoch 10/15\n",
      "684/684 [==============================] - 1s 1ms/step - loss: 0.0747 - accuracy: 0.9795 - val_loss: 1.0604 - val_accuracy: 0.8168\n",
      "Epoch 11/15\n",
      "684/684 [==============================] - 1s 1ms/step - loss: 0.0695 - accuracy: 0.9815 - val_loss: 1.0075 - val_accuracy: 0.8226\n",
      "Epoch 12/15\n",
      "684/684 [==============================] - 1s 1ms/step - loss: 0.0639 - accuracy: 0.9823 - val_loss: 1.0814 - val_accuracy: 0.7999\n",
      "Epoch 13/15\n",
      "684/684 [==============================] - 1s 1ms/step - loss: 0.0620 - accuracy: 0.9823 - val_loss: 1.0502 - val_accuracy: 0.8175\n",
      "Epoch 14/15\n",
      "684/684 [==============================] - 1s 1ms/step - loss: 0.0582 - accuracy: 0.9844 - val_loss: 1.0093 - val_accuracy: 0.8325\n",
      "Epoch 15/15\n",
      "684/684 [==============================] - 1s 1ms/step - loss: 0.0563 - accuracy: 0.9855 - val_loss: 1.0089 - val_accuracy: 0.8296\n"
     ]
    }
   ],
   "source": [
    "model = keras.models.Sequential(\r\n",
    "    name = 'Light_CNN_Augmented',\r\n",
    "    layers =\r\n",
    "    [\r\n",
    "        keras.layers.InputLayer(input_shape=input_dim),\r\n",
    "        keras.layers.Conv1D(16, 3, activation='relu'),\r\n",
    "        keras.layers.Conv1D(16, 3, activation='relu'),\r\n",
    "        keras.layers.Flatten(),\r\n",
    "        keras.layers.Dense(output_dim, activation=keras.activations.softmax),\r\n",
    "    ]\r\n",
    ")\r\n",
    "\r\n",
    "model.summary()\r\n",
    "model.compile(\r\n",
    "    optimizer=keras.optimizers.Adam(),\r\n",
    "    loss='categorical_crossentropy',\r\n",
    "    metrics=['accuracy'],\r\n",
    ")\r\n",
    "\r\n",
    "model_train_history[model] = model.fit(\r\n",
    "    x=x_train_augmented,\r\n",
    "    y=y_train_augmented,\r\n",
    "    epochs=epochs,\r\n",
    "    batch_size=16,\r\n",
    "    validation_split=validation_split,\r\n",
    "    shuffle=True,\r\n",
    "    verbose=1,\r\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KCZLR6W6NUFk",
    "outputId": "041f485a-b391-443b-ac78-516dab278cff"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"Hybrid_Augmented\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1d_4 (Conv1D)            (None, 16, 16)            112       \n",
      "_________________________________________________________________\n",
      "conv1d_5 (Conv1D)            (None, 14, 16)            784       \n",
      "_________________________________________________________________\n",
      "flatten_4 (Flatten)          (None, 224)               0         \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 128)               28800     \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (None, 64)                8256      \n",
      "_________________________________________________________________\n",
      "dense_11 (Dense)             (None, 20)                1300      \n",
      "=================================================================\n",
      "Total params: 55,764\n",
      "Trainable params: 55,764\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/15\n",
      "684/684 [==============================] - 2s 2ms/step - loss: 0.8132 - accuracy: 0.7384 - val_loss: 1.1376 - val_accuracy: 0.7107\n",
      "Epoch 2/15\n",
      "684/684 [==============================] - 1s 2ms/step - loss: 0.2218 - accuracy: 0.9267 - val_loss: 1.1774 - val_accuracy: 0.7462\n",
      "Epoch 3/15\n",
      "684/684 [==============================] - 1s 2ms/step - loss: 0.1512 - accuracy: 0.9532 - val_loss: 1.0959 - val_accuracy: 0.7835\n",
      "Epoch 4/15\n",
      "684/684 [==============================] - 1s 2ms/step - loss: 0.1145 - accuracy: 0.9639 - val_loss: 1.3354 - val_accuracy: 0.7377\n",
      "Epoch 5/15\n",
      "684/684 [==============================] - 1s 2ms/step - loss: 0.1019 - accuracy: 0.9667 - val_loss: 1.1903 - val_accuracy: 0.7751\n",
      "Epoch 6/15\n",
      "684/684 [==============================] - 1s 2ms/step - loss: 0.0843 - accuracy: 0.9744 - val_loss: 1.1088 - val_accuracy: 0.8076\n",
      "Epoch 7/15\n",
      "684/684 [==============================] - 1s 2ms/step - loss: 0.0720 - accuracy: 0.9766 - val_loss: 1.1928 - val_accuracy: 0.7879\n",
      "Epoch 8/15\n",
      "684/684 [==============================] - 1s 2ms/step - loss: 0.0595 - accuracy: 0.9833 - val_loss: 1.1801 - val_accuracy: 0.7853\n",
      "Epoch 9/15\n",
      "684/684 [==============================] - 1s 2ms/step - loss: 0.0539 - accuracy: 0.9842 - val_loss: 1.2253 - val_accuracy: 0.7966\n",
      "Epoch 10/15\n",
      "684/684 [==============================] - 2s 2ms/step - loss: 0.0597 - accuracy: 0.9817 - val_loss: 1.2329 - val_accuracy: 0.8091\n",
      "Epoch 11/15\n",
      "684/684 [==============================] - 2s 2ms/step - loss: 0.0502 - accuracy: 0.9846 - val_loss: 1.3763 - val_accuracy: 0.7798\n",
      "Epoch 12/15\n",
      "684/684 [==============================] - 1s 2ms/step - loss: 0.0486 - accuracy: 0.9847 - val_loss: 1.2473 - val_accuracy: 0.7999\n",
      "Epoch 13/15\n",
      "684/684 [==============================] - 1s 2ms/step - loss: 0.0507 - accuracy: 0.9848 - val_loss: 1.2375 - val_accuracy: 0.7992\n",
      "Epoch 14/15\n",
      "684/684 [==============================] - 1s 2ms/step - loss: 0.0414 - accuracy: 0.9871 - val_loss: 1.2952 - val_accuracy: 0.7955\n",
      "Epoch 15/15\n",
      "684/684 [==============================] - 1s 2ms/step - loss: 0.0389 - accuracy: 0.9882 - val_loss: 1.3309 - val_accuracy: 0.8032\n"
     ]
    }
   ],
   "source": [
    "model = keras.models.Sequential(\r\n",
    "    name = 'Hybrid_Augmented',\r\n",
    "    layers =\r\n",
    "    [\r\n",
    "        keras.layers.InputLayer(input_shape=input_dim),\r\n",
    "        keras.layers.Conv1D(16, 3, activation='relu'),\r\n",
    "        keras.layers.Conv1D(16, 3, activation='relu'),\r\n",
    "        keras.layers.Flatten(),\r\n",
    "        keras.layers.Dense(128, activation=keras.activations.relu),\r\n",
    "        keras.layers.Dense(128, activation=keras.activations.relu),\r\n",
    "        keras.layers.Dense(64, activation=keras.activations.relu),\r\n",
    "        keras.layers.Dense(output_dim, activation=keras.activations.softmax),\r\n",
    "    ]\r\n",
    ")\r\n",
    "\r\n",
    "model.summary()\r\n",
    "model.compile(\r\n",
    "    optimizer=keras.optimizers.Adam(),\r\n",
    "    loss='categorical_crossentropy',\r\n",
    "    metrics=['accuracy'],\r\n",
    ")\r\n",
    "\r\n",
    "model_train_history[model] = model.fit(\r\n",
    "    x=x_train_augmented,\r\n",
    "    y=y_train_augmented,\r\n",
    "    epochs=epochs,\r\n",
    "    batch_size=16,\r\n",
    "    validation_split=validation_split,\r\n",
    "    shuffle=True,\r\n",
    "    verbose=1,\r\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QPN7n_nOQdpE"
   },
   "source": [
    "### Regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FEtJgApiQc7C",
    "outputId": "eef8e910-bf47-471e-fa4b-c09a208f8471"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"Hybrid_Augmented_Regularized\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1d_6 (Conv1D)            (None, 16, 16)            112       \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 16, 16)            0         \n",
      "_________________________________________________________________\n",
      "batch_normalization (BatchNo (None, 16, 16)            64        \n",
      "_________________________________________________________________\n",
      "conv1d_7 (Conv1D)            (None, 14, 16)            784       \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 14, 16)            0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 14, 16)            64        \n",
      "_________________________________________________________________\n",
      "flatten_5 (Flatten)          (None, 224)               0         \n",
      "_________________________________________________________________\n",
      "dense_12 (Dense)             (None, 128)               28800     \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 128)               512       \n",
      "_________________________________________________________________\n",
      "dense_13 (Dense)             (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 128)               512       \n",
      "_________________________________________________________________\n",
      "dense_14 (Dense)             (None, 64)                8256      \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 64)                256       \n",
      "_________________________________________________________________\n",
      "dense_15 (Dense)             (None, 20)                1300      \n",
      "=================================================================\n",
      "Total params: 57,172\n",
      "Trainable params: 56,468\n",
      "Non-trainable params: 704\n",
      "_________________________________________________________________\n",
      "Epoch 1/15\n",
      "684/684 [==============================] - 4s 4ms/step - loss: 2.3149 - accuracy: 0.2778 - val_loss: 1.2543 - val_accuracy: 0.6576\n",
      "Epoch 2/15\n",
      "684/684 [==============================] - 2s 3ms/step - loss: 1.2960 - accuracy: 0.5488 - val_loss: 0.7668 - val_accuracy: 0.7721\n",
      "Epoch 3/15\n",
      "684/684 [==============================] - 2s 3ms/step - loss: 0.9847 - accuracy: 0.6494 - val_loss: 0.6301 - val_accuracy: 0.7893\n",
      "Epoch 4/15\n",
      "684/684 [==============================] - 2s 4ms/step - loss: 0.8262 - accuracy: 0.7142 - val_loss: 0.5676 - val_accuracy: 0.8230\n",
      "Epoch 5/15\n",
      "684/684 [==============================] - 2s 3ms/step - loss: 0.7150 - accuracy: 0.7517 - val_loss: 0.5876 - val_accuracy: 0.8171\n",
      "Epoch 6/15\n",
      "684/684 [==============================] - 2s 3ms/step - loss: 0.6527 - accuracy: 0.7778 - val_loss: 0.5391 - val_accuracy: 0.8292\n",
      "Epoch 7/15\n",
      "684/684 [==============================] - 2s 3ms/step - loss: 0.5940 - accuracy: 0.8005 - val_loss: 0.4826 - val_accuracy: 0.8632\n",
      "Epoch 8/15\n",
      "684/684 [==============================] - 2s 3ms/step - loss: 0.5628 - accuracy: 0.8169 - val_loss: 0.4542 - val_accuracy: 0.8570\n",
      "Epoch 9/15\n",
      "684/684 [==============================] - 2s 3ms/step - loss: 0.5210 - accuracy: 0.8275 - val_loss: 0.4527 - val_accuracy: 0.8756\n",
      "Epoch 10/15\n",
      "684/684 [==============================] - 2s 4ms/step - loss: 0.4961 - accuracy: 0.8386 - val_loss: 0.4424 - val_accuracy: 0.8698\n",
      "Epoch 11/15\n",
      "684/684 [==============================] - 2s 3ms/step - loss: 0.4838 - accuracy: 0.8427 - val_loss: 0.4758 - val_accuracy: 0.8658\n",
      "Epoch 12/15\n",
      "684/684 [==============================] - 2s 3ms/step - loss: 0.4603 - accuracy: 0.8559 - val_loss: 0.4454 - val_accuracy: 0.8713\n",
      "Epoch 13/15\n",
      "684/684 [==============================] - 2s 3ms/step - loss: 0.4307 - accuracy: 0.8680 - val_loss: 0.4485 - val_accuracy: 0.8723\n",
      "Epoch 14/15\n",
      "684/684 [==============================] - 2s 3ms/step - loss: 0.4298 - accuracy: 0.8666 - val_loss: 0.4395 - val_accuracy: 0.8756\n",
      "Epoch 15/15\n",
      "684/684 [==============================] - 2s 3ms/step - loss: 0.4127 - accuracy: 0.8725 - val_loss: 0.4558 - val_accuracy: 0.8698\n"
     ]
    }
   ],
   "source": [
    "model = keras.models.Sequential(\r\n",
    "    name = 'Hybrid_Augmented_Regularized',\r\n",
    "    layers =\r\n",
    "    [\r\n",
    "        keras.layers.InputLayer(input_shape=input_dim),\r\n",
    "        keras.layers.Conv1D(16, 3, activation='relu'),\r\n",
    "        keras.layers.Dropout(.2),\r\n",
    "        keras.layers.BatchNormalization(),\r\n",
    "        keras.layers.Conv1D(16, 3, activation='relu'),\r\n",
    "        keras.layers.Dropout(.2),\r\n",
    "        keras.layers.BatchNormalization(),\r\n",
    "        keras.layers.Flatten(),\r\n",
    "        keras.layers.Dense(128, activation=keras.activations.relu),\r\n",
    "        keras.layers.Dropout(.3),\r\n",
    "        keras.layers.BatchNormalization(),\r\n",
    "        keras.layers.Dense(128, activation=keras.activations.relu),\r\n",
    "        keras.layers.Dropout(.3),\r\n",
    "        keras.layers.BatchNormalization(),\r\n",
    "        keras.layers.Dense(64, activation=keras.activations.relu),\r\n",
    "        keras.layers.Dropout(.3),\r\n",
    "        keras.layers.BatchNormalization(),\r\n",
    "        keras.layers.Dense(output_dim, activation=keras.activations.softmax),\r\n",
    "    ]\r\n",
    ")\r\n",
    "\r\n",
    "model.summary()\r\n",
    "model.compile(\r\n",
    "    optimizer=keras.optimizers.Adam(),\r\n",
    "    loss='categorical_crossentropy',\r\n",
    "    metrics=['accuracy'],\r\n",
    ")\r\n",
    "\r\n",
    "model_train_history[model] = model.fit(\r\n",
    "    x=x_train_augmented,\r\n",
    "    y=y_train_augmented,\r\n",
    "    epochs=epochs,\r\n",
    "    batch_size=16,\r\n",
    "    validation_split=validation_split,\r\n",
    "    shuffle=True,\r\n",
    "    verbose=1,\r\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KerasTuner\r\n",
    "\r\n",
    "To find the best model possible, KerasTuner can be used. Below is an example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install keras_tuner\r\n",
    "import keras_tuner as kt\r\n",
    "import tensorflow as tf\r\n",
    "import numpy as np\r\n",
    "\r\n",
    "def build_model(hp):\r\n",
    "    \"\"\"Builds a convolutional model.\"\"\"\r\n",
    "    inputs = tf.keras.Input(shape=input_dim)\r\n",
    "    x = inputs\r\n",
    "\r\n",
    "    normalization_conv = hp.Choice(\"Normalization_conv\", [\"yes\", \"no\"])\r\n",
    "    normalization_dense = hp.Choice(\"Normalization_dense\", [\"yes\", \"no\"])\r\n",
    "    dropout_conv = hp.Choice(\"dropout_conv\", [\"yes\", \"no\"])\r\n",
    "    rate_dropout_conv = hp.Float(\"rate_dropout_conv\", .1, 0.5, step=.1, default=.2)\r\n",
    "    dropout_dense = hp.Choice(\"dropout_dense\", [\"yes\", \"no\"])\r\n",
    "    rate_dropout_dense = hp.Float(\"rate_dropout_dense\", .1, 0.5, step=.1, default=.3)\r\n",
    "    pooling = hp.Choice(\"pooling_conv\", [\"max\", \"avg\"])\r\n",
    "\r\n",
    "    for i in range(hp.Int(\"conv_layers\", 1, 3, default=3)):\r\n",
    "        x = tf.keras.layers.Conv1D(\r\n",
    "            filters=hp.Int(\"filters_\" + str(i), 8, 64, step=8, default=16),\r\n",
    "            kernel_size=hp.Int(\"kernel_size_\" + str(i), 2, 8, step=1, default=3),\r\n",
    "            activation=\"relu\",\r\n",
    "            padding=\"same\"\r\n",
    "        )(x)\r\n",
    "\r\n",
    "        if pooling == \"max\":\r\n",
    "            x = tf.keras.layers.MaxPooling1D()(x)\r\n",
    "        else:\r\n",
    "            x = tf.keras.layers.AveragePooling1D()(x)\r\n",
    "        \r\n",
    "        if dropout_conv == \"yes\":\r\n",
    "            x = tf.keras.layers.Dropout(\r\n",
    "                rate=rate_dropout_conv\r\n",
    "            )(x)\r\n",
    "    \r\n",
    "        if normalization_conv == \"yes\":\r\n",
    "            x = tf.keras.layers.BatchNormalization()(x)\r\n",
    "      \r\n",
    "        \r\n",
    "    x = tf.keras.layers.Flatten()(x)\r\n",
    "    \r\n",
    "    for i in range(hp.Int(\"dense_layers\", 1, 4, default=3)):\r\n",
    "        x = tf.keras.layers.Dense(\r\n",
    "            units=hp.Int(\"neurons_\" + str(i), 32, 256, step=32, default=128),\r\n",
    "            activation=\"relu\",\r\n",
    "        )(x)\r\n",
    "        if dropout_dense == \"yes\":\r\n",
    "            x = tf.keras.layers.Dropout(\r\n",
    "                rate=rate_dropout_dense\r\n",
    "            )(x)\r\n",
    "\r\n",
    "        if normalization_dense == \"yes\":\r\n",
    "            x = tf.keras.layers.BatchNormalization()(x)\r\n",
    "    \r\n",
    "    outputs = tf.keras.layers.Dense(output_dim, activation=\"softmax\")(x)\r\n",
    "\r\n",
    "    model = tf.keras.Model(inputs, outputs)\r\n",
    "\r\n",
    "    optimizer = hp.Choice(\"optimizer\", [\"adam\", \"sgd\"])\r\n",
    "    model.compile(\r\n",
    "        optimizer, loss=\"categorical_crossentropy\", metrics=[\"accuracy\"]\r\n",
    "    )\r\n",
    "    return model\r\n",
    "\r\n",
    "\r\n",
    "tuner = kt.Hyperband(\r\n",
    "    hypermodel=build_model,\r\n",
    "    objective=\"val_accuracy\",\r\n",
    "    max_epochs=epochs,\r\n",
    "    factor=2,\r\n",
    "    hyperband_iterations=10,\r\n",
    "    distribution_strategy=tf.distribute.MirroredStrategy(),\r\n",
    "    directory=\"results_dir\",\r\n",
    "    project_name=\"CNN\",\r\n",
    "    overwrite=True,\r\n",
    ")\r\n",
    "\r\n",
    "tuner.search(\r\n",
    "    x_train_augmented,\r\n",
    "    y_train_augmented,\r\n",
    "    epochs=epochs,\r\n",
    "    batch_size=32,\r\n",
    "    shuffle=True,\r\n",
    "    validation_split=validation_split,\r\n",
    "    callbacks=[tf.keras.callbacks.EarlyStopping(\"val_accuracy\")],\r\n",
    ")\r\n",
    "\r\n",
    "models = tuner.get_best_models(num_models=2)\r\n",
    "\r\n",
    "for model in models:\r\n",
    "    model.summary()\r\n",
    "    model.compile(\r\n",
    "        optimizer=keras.optimizers.Adam(),\r\n",
    "        loss='categorical_crossentropy',\r\n",
    "        metrics=['accuracy'],\r\n",
    "    )\r\n",
    "\r\n",
    "    model_train_history[model] = model.fit(\r\n",
    "        x=x_train_augmented,\r\n",
    "        y=y_train_augmented,\r\n",
    "        epochs=epochs,\r\n",
    "        batch_size=32,\r\n",
    "        validation_split=validation_split,\r\n",
    "        shuffle=True,\r\n",
    "        verbose=1,\r\n",
    "    )\r\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kiNFBvnWFFgS"
   },
   "source": [
    "### Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 369
    },
    "id": "8rdErix4SjlX",
    "outputId": "4812cf0b-e6b2-48d5-dcf9-20ea5ab6b248"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(14,5))\r\n",
    "colors_graph = plt.cm.get_cmap(\"Set2\", len(model_train_history))\r\n",
    "handles = []\r\n",
    "\r\n",
    "for i, (model, history) in enumerate(model_train_history.items()):\r\n",
    "    color = colors_graph(i)\r\n",
    "    label = '{} ({})'.format(model.name, model.count_params())\r\n",
    "    axs[0].plot(history.history['loss'], c=color, ls='-.', alpha=.9)\r\n",
    "    axs[1].plot(history.history['accuracy'], c=color, ls='-.', alpha=.9)\r\n",
    "    axs[0].plot(history.history['val_loss'], c=color)\r\n",
    "    axs[1].plot(history.history['val_accuracy'], c=color)\r\n",
    "    handles.append(mpatches.Patch(color=color, label=label))\r\n",
    "\r\n",
    "for ax in axs:\r\n",
    "    ax.set_xlabel('Epoch', fontsize=16)\r\n",
    "axs[0].set_ylabel('loss', fontsize=16)\r\n",
    "axs[0].set_yscale('log')\r\n",
    "axs[1].set_ylabel('accuracy', fontsize=16)\r\n",
    "axs[1].set_ylim(0.5,1.01)\r\n",
    "\r\n",
    "handles.append(Line2D([0], [0], color='grey', lw=1, ls='-', label='validation'))\r\n",
    "handles.append(Line2D([0], [0], color='grey', lw=1, ls='-.', label='training'))\r\n",
    "\r\n",
    "fig.subplots_adjust(right=0.85)\r\n",
    "fig.legend(handles=handles,\r\n",
    "           loc=\"center right\",\r\n",
    "           borderaxespad=1,\r\n",
    "           bbox_to_anchor=(.95,0.33))\r\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZhAVJHCifmYO"
   },
   "source": [
    "## Model export\r\n",
    "\r\n",
    "Once you have a good model, you can save it on your Google Drive or local files. A model information JSON file is also added to store labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8szkWLFDfmYP",
    "outputId": "0dcd2321-bbc5-48e9-9b59-84d858a6b349"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\r\n",
    "import json\r\n",
    "\r\n",
    "if IN_COLAB:\r\n",
    "    content_path = Path('/').absolute() / 'content'\r\n",
    "    drive_path = content_path / 'drive'\r\n",
    "    google.colab.drive.mount(str(drive_path))\r\n",
    "    save_path = drive_path / 'My Drive'\r\n",
    "    \r\n",
    "    for subfolder in ['Pose Classification Kit', 'Models']:\r\n",
    "        save_path /= subfolder\r\n",
    "        if not (save_path).is_dir():\r\n",
    "            %mkdir \"{save_path}\"\r\n",
    "else:\r\n",
    "    save_path = Path('.').absolute()\r\n",
    "    %mkdir \"{save_path}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "RXwTWIfS8t5o"
   },
   "outputs": [],
   "source": [
    "import itertools\r\n",
    "\r\n",
    "x_test_upper, y_test_upper = dataAugmentation(\r\n",
    "    dataset['x_test'], dataset['y_test_onehot'],\r\n",
    "    augmentation_ratio = 1.,\r\n",
    "    remove_specific_keypoints = np.where(np.isin(BODY18.mapping,[\r\n",
    "        \"left_knee\", \"right_knee\", \"left_ankle\", \"right_ankle\", \"left_hip\", \"right_hip\"\r\n",
    "        ]))[0],\r\n",
    ")\r\n",
    "\r\n",
    "def plot_cm(labels, confusion_matrix):\r\n",
    "    fig, ax = plt.subplots(figsize=(10,10), dpi=100)\r\n",
    "\r\n",
    "    ax.imshow(confusion_matrix, interpolation='nearest', cmap=plt.cm.Blues)\r\n",
    "    tick_marks = np.arange(len(labels))\r\n",
    "    ax.grid(False)\r\n",
    "    ax.set_xticks(tick_marks)\r\n",
    "    ax.set_xticklabels(labels, rotation=40, ha='right')\r\n",
    "    ax.set_yticks(tick_marks)\r\n",
    "    ax.set_yticklabels(labels)\r\n",
    "\r\n",
    "    thresh = np.max(confusion_matrix) / 2.\r\n",
    "    for i, j in itertools.product(range(confusion_matrix.shape[0]), range(confusion_matrix.shape[1])):\r\n",
    "        plt.text(j, i, confusion_matrix[i, j],\r\n",
    "            horizontalalignment=\"center\",\r\n",
    "            color=\"white\" if confusion_matrix[i, j] > thresh else \"black\")\r\n",
    "\r\n",
    "    fig.tight_layout()\r\n",
    "    ax.set_ylabel('True label', fontsize=12)\r\n",
    "    ax.set_xlabel('Predicted label', fontsize=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bX6hDhwe8lt5"
   },
   "source": [
    "## Light weight model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1tJr3fbYfmYP",
    "outputId": "604d1bec-c698-4c68-988f-510b647860e8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"LightWeight_CNN_BODY18\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1d_8 (Conv1D)            (None, 16, 12)            84        \n",
      "_________________________________________________________________\n",
      "conv1d_9 (Conv1D)            (None, 14, 12)            444       \n",
      "_________________________________________________________________\n",
      "flatten_6 (Flatten)          (None, 168)               0         \n",
      "_________________________________________________________________\n",
      "dense_16 (Dense)             (None, 20)                3380      \n",
      "=================================================================\n",
      "Total params: 3,908\n",
      "Trainable params: 3,908\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/15\n",
      "428/428 - 1s - loss: 1.8760 - accuracy: 0.5003 - val_loss: 0.8079 - val_accuracy: 0.7720\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.80791, saving model to /content/drive/My Drive/Pose Classification Kit/Models/LightWeight_CNN_BODY18.h5\n",
      "Epoch 2/15\n",
      "428/428 - 1s - loss: 0.5568 - accuracy: 0.8515 - val_loss: 0.4053 - val_accuracy: 0.8922\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.80791 to 0.40527, saving model to /content/drive/My Drive/Pose Classification Kit/Models/LightWeight_CNN_BODY18.h5\n",
      "Epoch 3/15\n",
      "428/428 - 0s - loss: 0.3462 - accuracy: 0.9061 - val_loss: 0.2890 - val_accuracy: 0.9080\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.40527 to 0.28896, saving model to /content/drive/My Drive/Pose Classification Kit/Models/LightWeight_CNN_BODY18.h5\n",
      "Epoch 4/15\n",
      "428/428 - 0s - loss: 0.2631 - accuracy: 0.9318 - val_loss: 0.2234 - val_accuracy: 0.9494\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.28896 to 0.22336, saving model to /content/drive/My Drive/Pose Classification Kit/Models/LightWeight_CNN_BODY18.h5\n",
      "Epoch 5/15\n",
      "428/428 - 0s - loss: 0.2135 - accuracy: 0.9453 - val_loss: 0.1846 - val_accuracy: 0.9627\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.22336 to 0.18459, saving model to /content/drive/My Drive/Pose Classification Kit/Models/LightWeight_CNN_BODY18.h5\n",
      "Epoch 6/15\n",
      "428/428 - 0s - loss: 0.1820 - accuracy: 0.9574 - val_loss: 0.1715 - val_accuracy: 0.9585\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.18459 to 0.17152, saving model to /content/drive/My Drive/Pose Classification Kit/Models/LightWeight_CNN_BODY18.h5\n",
      "Epoch 7/15\n",
      "428/428 - 1s - loss: 0.1578 - accuracy: 0.9637 - val_loss: 0.1419 - val_accuracy: 0.9701\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.17152 to 0.14186, saving model to /content/drive/My Drive/Pose Classification Kit/Models/LightWeight_CNN_BODY18.h5\n",
      "Epoch 8/15\n",
      "428/428 - 0s - loss: 0.1406 - accuracy: 0.9674 - val_loss: 0.1241 - val_accuracy: 0.9693\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.14186 to 0.12411, saving model to /content/drive/My Drive/Pose Classification Kit/Models/LightWeight_CNN_BODY18.h5\n",
      "Epoch 9/15\n",
      "428/428 - 0s - loss: 0.1205 - accuracy: 0.9737 - val_loss: 0.1179 - val_accuracy: 0.9726\n",
      "\n",
      "Epoch 00009: val_loss improved from 0.12411 to 0.11793, saving model to /content/drive/My Drive/Pose Classification Kit/Models/LightWeight_CNN_BODY18.h5\n",
      "Epoch 10/15\n",
      "428/428 - 0s - loss: 0.1087 - accuracy: 0.9773 - val_loss: 0.1101 - val_accuracy: 0.9776\n",
      "\n",
      "Epoch 00010: val_loss improved from 0.11793 to 0.11008, saving model to /content/drive/My Drive/Pose Classification Kit/Models/LightWeight_CNN_BODY18.h5\n",
      "Epoch 11/15\n",
      "428/428 - 0s - loss: 0.1056 - accuracy: 0.9775 - val_loss: 0.1034 - val_accuracy: 0.9768\n",
      "\n",
      "Epoch 00011: val_loss improved from 0.11008 to 0.10335, saving model to /content/drive/My Drive/Pose Classification Kit/Models/LightWeight_CNN_BODY18.h5\n",
      "Epoch 12/15\n",
      "428/428 - 0s - loss: 0.0886 - accuracy: 0.9835 - val_loss: 0.1110 - val_accuracy: 0.9743\n",
      "\n",
      "Epoch 00012: val_loss did not improve from 0.10335\n",
      "Epoch 13/15\n",
      "428/428 - 1s - loss: 0.0842 - accuracy: 0.9846 - val_loss: 0.0875 - val_accuracy: 0.9867\n",
      "\n",
      "Epoch 00013: val_loss improved from 0.10335 to 0.08746, saving model to /content/drive/My Drive/Pose Classification Kit/Models/LightWeight_CNN_BODY18.h5\n",
      "Epoch 14/15\n",
      "428/428 - 1s - loss: 0.0762 - accuracy: 0.9854 - val_loss: 0.1111 - val_accuracy: 0.9668\n",
      "\n",
      "Epoch 00014: val_loss did not improve from 0.08746\n",
      "Epoch 15/15\n",
      "428/428 - 0s - loss: 0.0716 - accuracy: 0.9862 - val_loss: 0.0724 - val_accuracy: 0.9818\n",
      "\n",
      "Epoch 00015: val_loss improved from 0.08746 to 0.07245, saving model to /content/drive/My Drive/Pose Classification Kit/Models/LightWeight_CNN_BODY18.h5\n"
     ]
    }
   ],
   "source": [
    "model_name = 'LightWeight_CNN_BODY18'\r\n",
    "model_path = save_path / '{name}.h5'.format(name = model_name)\r\n",
    "\r\n",
    "model = keras.models.Sequential(\r\n",
    "    name = model_name,\r\n",
    "    layers =\r\n",
    "    [\r\n",
    "        keras.layers.InputLayer(input_shape=input_dim),\r\n",
    "        keras.layers.Conv1D(12, 3, activation='relu'),\r\n",
    "        keras.layers.Conv1D(12, 3, activation='relu'),\r\n",
    "        keras.layers.Flatten(),\r\n",
    "        keras.layers.Dense(output_dim, activation=keras.activations.softmax),\r\n",
    "    ]\r\n",
    ")\r\n",
    "\r\n",
    "model.summary()\r\n",
    "model.compile(\r\n",
    "    optimizer=keras.optimizers.Adam(),\r\n",
    "    loss='categorical_crossentropy',\r\n",
    "    metrics=['accuracy'],\r\n",
    ")\r\n",
    "\r\n",
    "model.fit(\r\n",
    "    x=x_train,\r\n",
    "    y=y_train,\r\n",
    "    epochs=15,\r\n",
    "    batch_size=16,\r\n",
    "    validation_split=0.15,\r\n",
    "    shuffle=True,\r\n",
    "    callbacks=[keras.callbacks.ModelCheckpoint(filepath=model_path, verbose=2, save_best_only=True)],\r\n",
    "    verbose = 2,\r\n",
    ")\r\n",
    "\r\n",
    "with open(save_path / (model_name+'_info.json'), 'w') as f:\r\n",
    "    json.dump({'labels':dataset['labels']}, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gw8wue6VfmYQ",
    "outputId": "83b83be8-81a1-4297-85ad-e135172faf1e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LightWeight_CNN_BODY18 accuracy on full samples:\n",
      "63/63 [==============================] - 0s 929us/step - loss: 0.0583 - accuracy: 0.9825\n",
      "LightWeight_CNN_BODY18 accuracy on partial samples:\n",
      "63/63 [==============================] - 0s 903us/step - loss: 3.1793 - accuracy: 0.5095\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[3.179300546646118, 0.5095000267028809]"
      ]
     },
     "execution_count": 15,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = keras.models.load_model(model_path)\r\n",
    "\r\n",
    "print(model.name + \" accuracy on full samples:\")\r\n",
    "model.evaluate(x=dataset['x_test'], y=dataset['y_test_onehot'])\r\n",
    "print(model.name + \" accuracy on partial samples:\")\r\n",
    "model.evaluate(x=x_test_upper, y=y_test_upper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 994
    },
    "id": "Z-YoeSL34g1u",
    "outputId": "effd6ca7-5067-4330-c81a-3c1c2eb2394c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels_predict = model.predict(dataset['x_test'])\r\n",
    "cm = np.array(\r\n",
    "    tensorflow.math.confusion_matrix(\r\n",
    "        np.argmax(labels_predict,axis=1),\r\n",
    "        np.argmax(dataset['y_test_onehot'],axis=1)\r\n",
    "))\r\n",
    "\r\n",
    "plot_cm(labels = dataset['labels'], confusion_matrix = cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 994
    },
    "id": "QXqXKUOX6xEi",
    "outputId": "26107b7b-74d5-4250-81c0-56cb1c19fe4b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels_predict_upper = model.predict(x_test_upper)\r\n",
    "cm_upper = np.array(\r\n",
    "    tensorflow.math.confusion_matrix(\r\n",
    "        np.argmax(labels_predict_upper,axis=1),\r\n",
    "        np.argmax(y_test_upper,axis=1)\r\n",
    "))\r\n",
    "\r\n",
    "plot_cm(labels = dataset['labels'], confusion_matrix = cm_upper)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "icC2pnWs8xFe"
   },
   "source": [
    "## Most performant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zDmCrysL9HDJ",
    "outputId": "d53a4bfc-7254-4bc3-8f27-dc6556cb9f50"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"Robust_BODY18\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1d_10 (Conv1D)           (None, 16, 16)            112       \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 16, 16)            0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_5 (Batch (None, 16, 16)            64        \n",
      "_________________________________________________________________\n",
      "conv1d_11 (Conv1D)           (None, 14, 16)            784       \n",
      "_________________________________________________________________\n",
      "dropout_6 (Dropout)          (None, 14, 16)            0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_6 (Batch (None, 14, 16)            64        \n",
      "_________________________________________________________________\n",
      "flatten_7 (Flatten)          (None, 224)               0         \n",
      "_________________________________________________________________\n",
      "dense_17 (Dense)             (None, 128)               28800     \n",
      "_________________________________________________________________\n",
      "dropout_7 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_7 (Batch (None, 128)               512       \n",
      "_________________________________________________________________\n",
      "dense_18 (Dense)             (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "dropout_8 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_8 (Batch (None, 128)               512       \n",
      "_________________________________________________________________\n",
      "dense_19 (Dense)             (None, 64)                8256      \n",
      "_________________________________________________________________\n",
      "dropout_9 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_9 (Batch (None, 64)                256       \n",
      "_________________________________________________________________\n",
      "dense_20 (Dense)             (None, 20)                1300      \n",
      "=================================================================\n",
      "Total params: 57,172\n",
      "Trainable params: 56,468\n",
      "Non-trainable params: 704\n",
      "_________________________________________________________________\n",
      "Epoch 1/15\n",
      "727/727 - 3s - loss: 2.2377 - accuracy: 0.2892 - val_loss: 1.2828 - val_accuracy: 0.6251\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 1.28285, saving model to /content/drive/My Drive/Pose Classification Kit/Models/Robust_BODY18.h5\n",
      "Epoch 2/15\n",
      "727/727 - 2s - loss: 1.2801 - accuracy: 0.5342 - val_loss: 0.9059 - val_accuracy: 0.7094\n",
      "\n",
      "Epoch 00002: val_loss improved from 1.28285 to 0.90593, saving model to /content/drive/My Drive/Pose Classification Kit/Models/Robust_BODY18.h5\n",
      "Epoch 3/15\n",
      "727/727 - 2s - loss: 0.9708 - accuracy: 0.6528 - val_loss: 0.7362 - val_accuracy: 0.7796\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.90593 to 0.73621, saving model to /content/drive/My Drive/Pose Classification Kit/Models/Robust_BODY18.h5\n",
      "Epoch 4/15\n",
      "727/727 - 2s - loss: 0.8059 - accuracy: 0.7222 - val_loss: 0.6922 - val_accuracy: 0.7967\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.73621 to 0.69222, saving model to /content/drive/My Drive/Pose Classification Kit/Models/Robust_BODY18.h5\n",
      "Epoch 5/15\n",
      "727/727 - 2s - loss: 0.6782 - accuracy: 0.7751 - val_loss: 0.6168 - val_accuracy: 0.8323\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.69222 to 0.61683, saving model to /content/drive/My Drive/Pose Classification Kit/Models/Robust_BODY18.h5\n",
      "Epoch 6/15\n",
      "727/727 - 2s - loss: 0.6203 - accuracy: 0.7964 - val_loss: 0.5901 - val_accuracy: 0.8391\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.61683 to 0.59006, saving model to /content/drive/My Drive/Pose Classification Kit/Models/Robust_BODY18.h5\n",
      "Epoch 7/15\n",
      "727/727 - 2s - loss: 0.5891 - accuracy: 0.8074 - val_loss: 0.5427 - val_accuracy: 0.8313\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.59006 to 0.54271, saving model to /content/drive/My Drive/Pose Classification Kit/Models/Robust_BODY18.h5\n",
      "Epoch 8/15\n",
      "727/727 - 2s - loss: 0.5468 - accuracy: 0.8244 - val_loss: 0.5381 - val_accuracy: 0.8430\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.54271 to 0.53814, saving model to /content/drive/My Drive/Pose Classification Kit/Models/Robust_BODY18.h5\n",
      "Epoch 9/15\n",
      "727/727 - 2s - loss: 0.5050 - accuracy: 0.8358 - val_loss: 0.5649 - val_accuracy: 0.8347\n",
      "\n",
      "Epoch 00009: val_loss did not improve from 0.53814\n",
      "Epoch 10/15\n",
      "727/727 - 2s - loss: 0.4833 - accuracy: 0.8463 - val_loss: 0.4957 - val_accuracy: 0.8610\n",
      "\n",
      "Epoch 00010: val_loss improved from 0.53814 to 0.49566, saving model to /content/drive/My Drive/Pose Classification Kit/Models/Robust_BODY18.h5\n",
      "Epoch 11/15\n",
      "727/727 - 2s - loss: 0.4574 - accuracy: 0.8531 - val_loss: 0.4984 - val_accuracy: 0.8591\n",
      "\n",
      "Epoch 00011: val_loss did not improve from 0.49566\n",
      "Epoch 12/15\n",
      "727/727 - 2s - loss: 0.4333 - accuracy: 0.8635 - val_loss: 0.5184 - val_accuracy: 0.8576\n",
      "\n",
      "Epoch 00012: val_loss did not improve from 0.49566\n",
      "Epoch 13/15\n",
      "727/727 - 2s - loss: 0.4384 - accuracy: 0.8673 - val_loss: 0.4993 - val_accuracy: 0.8552\n",
      "\n",
      "Epoch 00013: val_loss did not improve from 0.49566\n",
      "Epoch 14/15\n",
      "727/727 - 2s - loss: 0.3960 - accuracy: 0.8752 - val_loss: 0.4922 - val_accuracy: 0.8654\n",
      "\n",
      "Epoch 00014: val_loss improved from 0.49566 to 0.49224, saving model to /content/drive/My Drive/Pose Classification Kit/Models/Robust_BODY18.h5\n",
      "Epoch 15/15\n",
      "727/727 - 2s - loss: 0.4260 - accuracy: 0.8661 - val_loss: 0.4847 - val_accuracy: 0.8635\n",
      "\n",
      "Epoch 00015: val_loss improved from 0.49224 to 0.48472, saving model to /content/drive/My Drive/Pose Classification Kit/Models/Robust_BODY18.h5\n"
     ]
    }
   ],
   "source": [
    "model_name = 'Robust_BODY18'\r\n",
    "model_path = save_path / '{name}.h5'.format(name = model_name)\r\n",
    "\r\n",
    "model = keras.models.Sequential(\r\n",
    "    name = model_name,\r\n",
    "    layers =\r\n",
    "    [\r\n",
    "        keras.layers.InputLayer(input_shape=input_dim),\r\n",
    "        keras.layers.Conv1D(16, 3, activation='relu'),\r\n",
    "        keras.layers.Dropout(.2),\r\n",
    "        keras.layers.BatchNormalization(),\r\n",
    "        keras.layers.Conv1D(16, 3, activation='relu'),\r\n",
    "        keras.layers.Dropout(.2),\r\n",
    "        keras.layers.BatchNormalization(),\r\n",
    "        keras.layers.Flatten(),\r\n",
    "        keras.layers.Dense(128, activation=keras.activations.relu),\r\n",
    "        keras.layers.Dropout(.3),\r\n",
    "        keras.layers.BatchNormalization(),\r\n",
    "        keras.layers.Dense(128, activation=keras.activations.relu),\r\n",
    "        keras.layers.Dropout(.3),\r\n",
    "        keras.layers.BatchNormalization(),\r\n",
    "        keras.layers.Dense(64, activation=keras.activations.relu),\r\n",
    "        keras.layers.Dropout(.3),\r\n",
    "        keras.layers.BatchNormalization(),\r\n",
    "        keras.layers.Dense(output_dim, activation=keras.activations.softmax),\r\n",
    "    ]\r\n",
    ")\r\n",
    "\r\n",
    "model.summary()\r\n",
    "model.compile(\r\n",
    "    optimizer=keras.optimizers.Adam(),\r\n",
    "    loss='categorical_crossentropy',\r\n",
    "    metrics=['accuracy'],\r\n",
    ")\r\n",
    "\r\n",
    "model.fit(\r\n",
    "    x=x_train_augmented,\r\n",
    "    y=y_train_augmented,\r\n",
    "    epochs=15,\r\n",
    "    batch_size=16,\r\n",
    "    validation_split=0.15,\r\n",
    "    shuffle=True,\r\n",
    "    callbacks=[keras.callbacks.ModelCheckpoint(filepath=model_path, verbose=2, save_best_only=True)],\r\n",
    "    verbose = 2,\r\n",
    ")\r\n",
    "\r\n",
    "with open(save_path / (model_name+'_info.json'), 'w') as f:\r\n",
    "    json.dump({'labels':dataset['labels']}, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "s50h5kVy-qw7",
    "outputId": "1f650a36-f2ce-453e-9998-0e7e40d22c25"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Robust_BODY18 accuracy on full samples:\n",
      "63/63 [==============================] - 0s 2ms/step - loss: 0.0567 - accuracy: 0.9830\n",
      "Robust_BODY18 accuracy on partial samples:\n",
      "63/63 [==============================] - 0s 1ms/step - loss: 0.1521 - accuracy: 0.9505\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.1520567387342453, 0.9505000114440918]"
      ]
     },
     "execution_count": 19,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(model.name + \" accuracy on full samples:\")\r\n",
    "model.evaluate(x=dataset['x_test'], y=dataset['y_test_onehot'])\r\n",
    "print(model.name + \" accuracy on partial samples:\")\r\n",
    "model.evaluate(x=x_test_upper, y=y_test_upper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 994
    },
    "id": "AOyqRbV1-u60",
    "outputId": "311a6306-bb67-449e-eded-d14c5f1a5c59"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels_predict = model.predict(dataset['x_test'])\r\n",
    "cm = np.array(\r\n",
    "    tensorflow.math.confusion_matrix(\r\n",
    "        np.argmax(labels_predict,axis=1),\r\n",
    "        np.argmax(dataset['y_test_onehot'],axis=1)\r\n",
    "))\r\n",
    "\r\n",
    "plot_cm(labels = dataset['labels'], confusion_matrix = cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 994
    },
    "id": "23h5uzgl-1zP",
    "outputId": "065686b2-10bf-4075-82f1-a7c055baf667"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels_predict_upper = model.predict(x_test_upper)\r\n",
    "cm_upper = np.array(\r\n",
    "    tensorflow.math.confusion_matrix(\r\n",
    "        np.argmax(labels_predict_upper,axis=1),\r\n",
    "        np.argmax(y_test_upper,axis=1)\r\n",
    "))\r\n",
    "\r\n",
    "plot_cm(labels = dataset['labels'], confusion_matrix = cm_upper)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KerasTuner\n",
    "\n",
    "🚧 Work in progress 🚧"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_name = 'KerasTuner'\r\n",
    "model_path = save_path / '{name}.h5'.format(name = model_name)\r\n",
    "\r\n",
    "model = keras.models.Sequential(\r\n",
    "    name = model_name,\r\n",
    "    layers =\r\n",
    "    [\r\n",
    "        keras.layers.InputLayer(input_shape=input_dim),\r\n",
    "        keras.layers.Conv1D(56, 2, activation='relu'),\r\n",
    "        keras.layers.MaxPooling1D();\r\n",
    "        keras.layers.Dropout(.2),\r\n",
    "        keras.layers.BatchNormalization(),\r\n",
    "        keras.layers.Flatten(),\r\n",
    "        keras.layers.Dense(96, activation=keras.activations.relu),\r\n",
    "        keras.layers.Dense(64, activation=keras.activations.relu),\r\n",
    "        keras.layers.Dense(output_dim, activation=keras.activations.softmax),\r\n",
    "    ]\r\n",
    ")\r\n",
    "\r\n",
    "model.summary()\r\n",
    "model.compile(\r\n",
    "    optimizer=keras.optimizers.Adam(),\r\n",
    "    loss='categorical_crossentropy',\r\n",
    "    metrics=['accuracy'],\r\n",
    ")\r\n",
    "\r\n",
    "model.fit(\r\n",
    "    x=x_train_augmented,\r\n",
    "    y=y_train_augmented,\r\n",
    "    epochs=15,\r\n",
    "    batch_size=16,\r\n",
    "    validation_split=0.15,\r\n",
    "    shuffle=True,\r\n",
    "    callbacks=[keras.callbacks.ModelCheckpoint(filepath=model_path, verbose=2, save_best_only=True)],\r\n",
    "    verbose = 2,\r\n",
    ")\r\n",
    "\r\n",
    "with open(save_path / (model_name+'_info.json'), 'w') as f:\r\n",
    "    json.dump({'labels':dataset['labels']}, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(model.name + \" accuracy on full samples:\")\r\n",
    "model.evaluate(x=dataset['x_test'], y=dataset['y_test_onehot'])\r\n",
    "print(model.name + \" accuracy on partial samples:\")\r\n",
    "model.evaluate(x=x_test_upper, y=y_test_upper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels_predict = model.predict(dataset['x_test'])\r\n",
    "cm = np.array(\r\n",
    "    tensorflow.math.confusion_matrix(\r\n",
    "        np.argmax(labels_predict,axis=1),\r\n",
    "        np.argmax(dataset['y_test_onehot'],axis=1)\r\n",
    "))\r\n",
    "\r\n",
    "plot_cm(labels = dataset['labels'], confusion_matrix = cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels_predict_upper = model.predict(x_test_upper)\r\n",
    "cm_upper = np.array(\r\n",
    "    tensorflow.math.confusion_matrix(\r\n",
    "        np.argmax(labels_predict_upper,axis=1),\r\n",
    "        np.argmax(y_test_upper,axis=1)\r\n",
    "))\r\n",
    "\r\n",
    "plot_cm(labels = dataset['labels'], confusion_matrix = cm_upper)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "include_colab_link": true,
   "name": "BodyModel-Creation.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
